System and method for assessing worker performance

ABSTRACT

Logistical operations (e.g., warehouses) may use a voice-enabled workflow to facilitate the work tasks of a staff (i.e., population) of workers. Typically, it is necessary for a worker to travel from location-to-location to complete assigned work tasks. As such, a worker&#39;s time spent travelling often correlates with the worker&#39;s overall work performance. Understanding the worker&#39;s travel performance is highly desirable, but computing a fair and accurate travel-performance metric is difficult. One reason for this is that the distance a worker travels is often unknown. The present invention embraces a system and method for accurately and fairly assessing a worker&#39;s travel performance by analyzing the worker&#39;s voice dialog.

FIELD OF THE INVENTION

The present invention relates to systems and methods for assessing worker performance and, more specifically, to determining travel-performance metrics for workers using voice-enabled mobile terminals in a warehouse setting.

BACKGROUND

The storage and movement of items in a warehouse is commonly managed by a warehouse management system (WMS). The WMS may create and manage warehouse work tasks (e.g., picking, stocking, etc.). In some cases, the WMS is interactive. As such, the WMS can guide workers through a workflow and detect errors in the process.

A WMS typically includes a plurality of mobile terminals in communication (e.g., wireless communication) with a centralized host computer. The mobile terminals may be worn or carried by a worker and used to facilitate warehouse work tasks, such as picking. For example, a mobile terminal may be used to scan barcodes on items that are gathered (i.e., picked) from storage locations for shipping. The mobile terminal may transmit the scanned data to the host computer, where WMS software running on the host computer receives the scanned data and logs the pick. Data from the host computer may also be transmitted to the mobile terminal. For example, after a pick is logged, the WMS software may assign a worker a new work task. A message regarding this work task may be transmitted from the host computer to the mobile terminal, which communicates the message to the worker.

One particularly efficient WMS utilizes voice-enabled mobile terminals to implement a voice-enabled workflow. The voice-enabled mobile terminals provide a speech interface between the host computer and the workers. A bi-directional communication via voice (i.e., a voice dialog) may be exchanged between the voice-enabled mobile terminal and the centralized host computer. Information transmitted by the host computer and received by a voice-enabled mobile terminal may be translated from text into voice prompts (e.g., questions, commands, instructions, statements, etc.) and transmitted to the worker via the voice-enabled mobile terminal's sound transducer (e.g., speaker). A worker may respond to a voice prompt by speaking a voice reply into the voice-enabled mobile terminal's microphone. In this way, voice-enabled workflow using voice-enabled mobile terminals provide an advantage over systems requiring other forms of workflow communication. Specifically, a voice-enabled workflow frees the worker's hands since no cumbersome equipment or paperwork is necessary to interact with the WMS.

Typically, the voice-enabled mobile terminal includes a headset worn by a worker. The voice-enabled mobile terminal also includes a mobile computing device (MCD). The MCD may be integrated within the headset or communicatively coupled to the headset and worn by the user (e.g., worn via a belt clip). The headset has a microphone for receiving voice sounds and a speaker for emitting voice prompts and sounds. Using the headset, a worker is able to receive voice instructions regarding assigned work tasks, ask questions, report the progress of work tasks, and report working conditions such as inventory shortages.

Workers may perform work tasks (e.g., picking) at different rates, and understanding a worker's voice-enabled workflow performance is important for optimizing the efficiency of a staff of workers. One traditional metric for measuring performance is the total number of work tasks completed in a shift (e.g., total number of picks). Unfortunately, this metric may be misleading. For example, a worker who works a longer shift will typically perform more picks than a worker who works a shorter shift. Here a better metric would seem to be a work-task rate (e.g., pick rate). Here again, however, this metric may be misleading. For example, if during a shift, a worker must repeatedly travel long distances on foot to pick various items, then the total number of items picked during the worker's shift may seem low compared to others. In general, properly assessing a worker's workflow performance is easily complicated by the particulars of the worker's work tasks and worker's environment. Therefore, a need exists for accurate and fair performance metrics to assess a worker's performance.

The time an exemplary warehouse-picking worker spends at work may be classified in three general ways: (i) time spent traveling, (ii) time spent picking, and (iii) time spent otherwise (e.g., breaks). Certain systems and methods for assessing worker performance by analyzing the worker's time spent picking are set forth in the commonly assigned U.S. patent application Ser. No. 14/880,482, and certain exemplary systems and methods for assessing by analyzing the worker's time spent otherwise are set forth in the commonly assigned U.S. patent application Ser. No. 14/861,270 (Each of U.S. patent application Ser. Nos. 14/880,482 and 14/861,270 is hereby incorporated by reference in its entirety and not just to the extent that it discloses the aforementioned exemplary systems and methods). The present disclosure embraces assessing worker performance by analyzing the time spent travelling.

Comparing workers based on travel time can be difficult. For example, a worker may be assigned work tasks having long location-to-location distances. In this case, a long travel time may not imply poor performance. Knowledge of the worker's distance travelled could reveal this fact, but unfortunately, travel distances are typically not available in the data available for analysis (e.g., the worker's voice dialog). Further, creating detailed maps of a warehouse that correlate distances to location-to-location movements are not convenient since the warehouse environment may often change. Therefore, a need exists for an accurate and fair travel-performance metric to assess a worker's travel time performance (i.e., travel performance) derived from a voice-dialog in a voice-enabled workflow that is independent of the distance a worker travels.

SUMMARY

Accordingly, in one aspect, the present invention embraces a worker management system. The worker management system includes a plurality of voice-enabled mobile terminals that are used by a population of workers. Each worker in the population of workers uses a particular voice-enabled mobile terminal to participate in a voice dialog corresponding to the worker's work tasks. The system also includes a host computer that is in wireless communication with the voice-enabled mobile terminals. The host computer includes a processor that is configured by software to receive voice dialogs from the population of workers during a measurement period. The processor is also configured to analyze each worker's voice dialog to obtain worker-travel times for each worker's work tasks. Then, using the worker-travel times and a model retrieved from the host computer's memory, the processor is configured to compute a travel-performance metric for each worker. Finally, the processor is configured to assess the performance of a particular worker by comparing the travel-performance metric for the particular worker to the travel-performance metrics for other workers in the population of workers.

In an exemplary embodiment of the worker-management system, computing a travel-performance metric for each worker includes computing a travel-pick ratio, which is the ratio of the worker's total travel time to the worker's total number of picks.

In another exemplary embodiment of the worker-management system, computing a travel-performance metric for each worker includes computing a travel-work ratio, which is the ratio of a worker's total travel time to the worker's time spent otherwise.

In another exemplary embodiment of the worker-management system, computing a travel-performance metric for each worker includes computing an effective-travel ratio, which is the ratio of a worker's travel time that resulted in a pick to the worker's total travel time.

In another exemplary embodiment of the worker-management system, computing a travel-performance metric for each worker includes comparing a worker-average-travel time to a population-average-travel time for a location-to-location movement. In this embodiment, the worker-average-travel time is the average of the worker-travel times obtained from a worker for the location-to-location movement, and the population-average-travel time is the average of the worker-travel times obtained from all workers in the population of workers for the location-to-location movement.

In another exemplary embodiment of the worker-management system, computing a travel-performance metric for each worker includes comparing a worker-total-travel time to a population-total-travel time for a location-to-location movement. In this embodiment, the worker-total-travel time is computed by summing the worker-travel times obtained from a worker for a location-to-location movement. The population-total-travel time is computed by counting the number of times the worker performed the location-to-location movement, and then multiplying this count with the average of the worker-travel times obtained from all workers in the population of workers for the location-to-location movement.

In another exemplary embodiment of the worker-management system, the processor is further configured by software to create a voice message for a particular worker based on the performance assessment, and then transmit the voice message from the host computer to the particular worker's voice-enabled mobile terminal.

In another exemplary embodiment of the worker-management system, the worker-management system includes a display that is communicatively coupled to host computer for presenting reports and/or alerts based on the assessment. In one possible embodiment, these reports and/or alerts include a ranking of workers by travel-performance metric. In another possible embodiment, these reports and/or alerts include a message that a worker needs attention regarding the worker's performance.

In another exemplary embodiment of the worker-management system, the population of workers is a subset of all workers that performed work during the measurement period.

In another exemplary embodiment of the worker-management system, the processor is further configured by software to record the travel-performance metrics, computed for each worker during the measurement period, in a database that is stored in a computer-readable memory.

In another aspect, the present invention embraces a method for assessing a worker's performance in a voice-enabled workflow. The method begins with the step of receiving a voice dialog corresponding to a worker's voice-enabled workflow. Next, the dialog is analyzed to obtain worker-travel times for each location-to-location movement performed by the worker during a measurement period. These steps (i.e., the steps of receiving and analyzing) are repeated to obtain worker-travel times for each worker in a population of workers. After the worker-travel times are obtained, a population-average-travel time for each location-to-location movement is created. The population-average-travel time for a particular location-to-location movement is the average of all worker-travel times obtained from the population of workers for the particular location-to-location movement. Next, using the worker-travel times and the population-average-travel times, a travel-performance metric is calculated for each worker. Finally, a worker's performance is assessed by comparing the worker's travel-performance metric to the travel-performance metrics for other workers in the population of workers.

In an exemplary embodiment of the method, the step of calculating a travel-performance metric for each worker includes computing, for each worker, the average difference between worker-average-travel times and population-average-travel-times for all location-to-location movements. In this case, a worker's worker-average-travel time for a particular location-to-location movement is the average of the worker's worker-travel times obtained for the particular location-to-location movement.

In another exemplary embodiment of the method, the step of calculating a travel-performance metric for each worker includes several steps. First, a worker-total-travel time is created for each location-to-location movement and for each worker. Here, the worker-total-travel time for a particular location-to-location movement is the sum of the worker's worker-travel times obtained for the particular location-to-location movement. Next, the number of times each location-to-location movement was performed by each worker is counted. Then, a population-total-travel time is created for each worker and for each location-to-location movement. In this case, a worker's population-total-travel time for a particular location-to-location movement is the number of times the particular location-to-location movement was performed by the worker multiplied by the population-average-travel time for the particular location-to-location movement. Finally, the travel-performance metric for each worker is calculated as the difference between the sum of the worker-total-travel times for all location-to-location movements and the sum of the population-total-travel times for all location-to-location movements divided by the total number of location-to-location movements performed by the worker during the measurement period.

In another exemplary embodiment of the method, the step of assessing the worker's performance includes combining the travel-performance metric with other performance metrics to generate a new performance metric.

In another exemplary embodiment of the method, the step of assessing the worker's voice-enabled workflow performance includes ranking workers in the population of workers by their travel-performance metric and determining the worker's performance by the worker's rank.

In another exemplary embodiment of the method, the step of assessing the worker's performance includes comparing the travel-performance metric for a worker obtained during the measurement period to a travel-performance metric for the worker obtained during a different measurement period.

In another exemplary embodiment of the method, the method further includes the steps of generating a graphical report, including the results of the assessment, and transmitting the graphical report to a computing device with a display for displaying the graphical report.

In another exemplary embodiment of the method, the method further includes the step of adjusting the work tasks assigned to a worker based on the assessment of the worker's performance.

The foregoing illustrative summary, as well as other exemplary objectives and/or advantages of the invention, and the manner in which the same are accomplished, are further explained within the following detailed description and its accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 graphically illustrates an exemplary implementation of a work management system in an exemplary warehouse according to an embodiment of the present invention.

FIG. 2 graphically depicts an exemplary implementation of a voice-enabled mobile terminal according to an embodiment of the present invention.

FIG. 3 graphically illustrates an exemplary process to obtain worker-travel times for an exemplary voice-enabled workflow according to an embodiment of the present invention.

FIG. 4 graphically illustrates the computation of a worker-average-travel time and a population-average-travel time according to an embodiment of the present invention.

FIG. 5 graphically illustrates the computation of a worker-total-travel time and a population-total-travel time according to an embodiment of the present invention.

FIG. 6 graphically depicts and exemplary report of travel-performance metrics for a population of workers according to an embodiment of the present invention.

DETAILED DESCRIPTION

The present invention embraces a system and a method for assessing a worker's performance in a voice-enabled workflow for a logistics operation (e.g., a warehouse). A worker's time spent traveling from location-to-location during work is a significant portion of the worker's total work time. As a result, an analysis of a worker's travel time is important for assessing a worker's performance.

A worker that performs work tasks (e.g., picking, stocking, etc.) in a voice-enabled workflow creates a voice dialog. The voice dialog contains data (e.g., times, locations, quantities, work-task type, etc.) corresponding to the worker's assigned work tasks (e.g., picking, stocking, etc.). As a result, the voice dialog from a worker may be recorded during a measurement period and then analyzed to create a travel-performance metric summarizing a worker's travel performance (e.g., speed, efficiency, accuracy, etc.).

A travel-performance metric is typically a single numerical value (e.g., a positive or negative number) representing the worker's performance relative to some group and/or time. For example, a travel-performance metric may represent the worker's performance relative to a particular group of workers (i.e., population) during a particular time (i.e., measurement period).

A worker's performance may be assessed by comparing the worker's travel-performance metric to travel-performance metrics of other workers and/or from other measurement periods. Various comparisons of may be made. In one example, a worker's travel-performance may be compared to the travel-performance metric of other workers in a population of workers for a particular measurement period. In another example, a worker's travel-performance from one measurement period may be compared to the same worker's travel-performance metric from another measurement period (or periods). Likewise, the metrics of a group of workers may be compared to the metrics of other groups of workers (e.g., different shifts of workers, different locations, etc.). In addition, trends and/or variations of a worker's (or group's) metrics over time may be derived. Other possible metric comparisons exist (e.g., between groups, individuals, measurement periods, work tasks, etc.) and are all within the scope of the present disclosure.

As means of example, FIG. 1 graphically depicts workers 2 operating in an exemplary warehouse 1. The workers 2 wear and use voice-enabled mobile terminals to wirelessly communicate (via voice prompts and responses) to a host computer 3 running software to manage the voice-enabled workflow.

The workers 2 in the warehouse, shown in FIG. 1, participate in a voice dialog to facilitate work tasks. As mentioned, the voice dialog typically includes the prompts generated by the host computer 3 and responses uttered by the worker 2. By way of example, consider the following exemplary portion of a voice dialog corresponding to FIG. 1:

-   -   Mobile Terminal: “Go to room 1, aisle 2, slot 2” (i.e., location         “A”);     -   Worker: “331” (i.e., check-digit to confirm location);     -   Mobile Terminal: “Pick two.”;     -   Worker: “Two” (i.e., confirms pick task);     -   Mobile Terminal: “Go to aisle 3, slot 5” (i.e., location “B”);     -   Worker: “225”;     -   Mobile Terminal: “Pick three.”;     -   Worker: “3”;     -   Mobile Terminal: “Go to aisle 4, slot 1” (i.e., location “C”).

The host computer may save to memory the voice dialog collected over some period of time (e.g., as UTF-8 alphanumeric text strings). Software running on the host computer may configure the host computer's processor to isolate the relevant portions of the voice dialog by identifying key words or phrases relating to work tasks. For example, in the above exemplary voice dialog, each captured expression can be uniquely identified and parsed into its constituent components, including (but not limited to) the following information:

-   -   The locations to which the user was directed;     -   The travel time between locations, as calculated by the time         between the current travel prompt (e.g., “Go to aisle 3, slot 5)         and the user-spoken check-digit (e.g., “225”); and     -   The time at the slot, as calculated by the time from the spoken         check-digit (e.g., “225”) to the completion of the task (e.g.,         “3”).

The voice dialog, including this information, may be stored in a computer readable memory (e.g., the host computer's memory) for later analysis or re-analysis.

The host computer 3 may be one or more, computers having software stored thereon. The host computer 3 may be any of a variety of different computers, including both client and server computers working together and including databases and/or systems necessary to interface with multiple voice-enabled mobile terminals. The host computer 3 may be located at one facility or may be distributed at geographically distinct facilities. Furthermore, the host computer 3 may include a proxy server. Therefore, the host computer 3 is not limited in scope to a specific configuration.

The host computer 3 may run one or more software programs for handling a particular task or set of tasks, such as inventory and warehouse management systems (which are available in various commercial forms). The host computer 3 may include a Warehouse Management System (WMS), a database, and a web application to facilitate the voice enabled workflow. The host computer 3 may also include software for programming and managing the individual voice-directed mobile terminals, as well as the software for analyzing the performance of workers.

FIG. 2 graphically depicts an implementation of a voice-enabled mobile terminal used in accordance with the voice-enabled workflow according an embodiment of the present disclosure. The voice-directed mobile terminal may be worn by a worker 2 or other user/operator (e.g. manager, supervisor, etc.), thereby allowing for hands-free operation. The voice-enabled mobile terminal typically includes a mobile computing device 10 and a headset 11. The mobile computing device 10 may be worn (e.g., on a belt) or otherwise used as part of a worker's normal work process (e.g., incorporated with a tool, a vehicle, or a device that the worker uses during work). The use of the descriptive term “terminal” is not limiting and may include any similar computer, device, machine, smartphone, smartwatch, indicia reader, or system. Therefore, the exact form of the voice-directed mobile terminal utilized to practice the present systems and methods is not limited to the embodiment shown in FIG. 2.

The headset 11, as shown in FIG. 2, serves as the worker's interface. The mobile computing device 10 may be communicatively coupled with the headset 11 or may be incorporated into the body of the headset 11. When separate, the headset 11 may be coupled to the mobile computing device 10 with a cord or via a wireless communication link (e.g., BLUETOOTH™). The headset 11 is worn (i.e., on the worker's head) and includes a microphone 12 for receiving the worker's voice. A headset speaker 13 transmits voice prompts (e.g., commands, instructions, descriptions, etc.) to the worker. The voice-enabled mobile terminal thus facilitates a voice dialog between the worker 2 and the host computer 3 to enable voice-directed movement throughout a warehouse or other facility.

The mobile computing device 10 may include the processing and memory necessary to convert the voice signals from the worker into data (e.g., UTF-8 alphanumeric text strings) suitable for transmission over a network (e.g., using speech-recognition software) and to convert the data received over a network into voice signals (e.g., using text-to-speech software). In some cases, the mobile computing device 10 may allow a worker 2 to perform a workflow without communication with a host computer 3. Therefore, various aspects of the present disclosure might be handled using voice-enabled mobile terminals only. Usually, however, the host computer 3 is desirable due to the complexity of voice-enabled workflow.

Each voice-enabled mobile terminal may communicate with the host computer 3 using a wireless communication link 4. The wireless communication link may use an appropriate wireless communication protocol (e.g., 802.11b/g/n, HTTP, TCP/IP, etc.) and may use one or more wireless access points that are coupled to the host computer 3 and accessed by the voice-directed mobile terminal.

By way of example, consider the voice-enable workflow as shown in FIG. 1. The voice dialogs 20 corresponding to a worker's 2 voice-enabled workflow are forwarded to the host computer 3 where they are stored for analysis. After a measurement period, the voice-dialogs, as shown in FIG. 3, are analyzed by software algorithms running on the host computer to isolate those portions of the voice dialog relating to location-to-location movements. The algorithms typically isolate the relevant portions of workflow dialog by identifying key words or phrases relating to location-to-location movement (e.g., travel prompts, check-digits, etc.). Since the keywords or phrases in the voice dialogs are recorded with timestamps (e.g., using a clock in the host computer or voice-enabled mobile terminal), worker-travel times for location-to-location movements may be obtained. FIG. 3 illustrates this process for a population of three workers moving within an environment (e.g., warehouse) having three exemplary locations: A, B, and C. The voice dialogs 20 from a population of workers 21 are analyzed 22 to obtain the times each worker (i.e., worker1, worker2, and worker3) took to perform each location-to-location movement (i.e., ab, bc, and ac). These worker-travel times are recorded (e.g., in a database) 23 for each worker (i.e., worker1, worker2, and worker3) and for each instance that the worker performed the movement 24.

It will be appreciated by a person of ordinary skill in the art that, although exemplary embodiments presented herein incorporate voice-enabled workflow, the present disclosure is not limited to voice. The present disclosure embraces any terminal that facilitates a dialog between a computer and a worker (e.g., speech, text, gestures, etc.).

The software running on the host computer use the worker-travel times, for a population of workers obtained during a measurement period, to compute a travel-performance metric for each worker. The travel-performance metric quantifies the worker's performance (e.g., speed, efficiency, etc.) in travelling to complete the worker's assigned work tasks. It is also possible to compute, from the dialog, the number of times a particular work task was completed during the measurement period. For example, how many times a particular location-to-location movement was performed or how many times a work-task was performed (e.g., number of picks).

Different travel-performance metrics may be used to assess a worker's performance. For example, in one embodiment the travel-performance metric is a travel-pick ratio (TPR) as shown below:

${TPR}_{{worker},{period}} = \frac{({Travel\_ Time})_{total}}{\left( {\#{of}\mspace{14mu}{Picks}} \right)_{total}}$

The TPR is the ratio of the worker's total travel time to the worker's total number of picks.

In another embodiment, the travel performance metric is a travel-work ratio (TWR) as shown below:

${TWR}_{{worker},{period}} = \frac{({Travel\_ Time})_{total}}{({Other\_ Time})_{total}}$

The TWR is the ratio of a worker's total travel time to the worker's time spent otherwise.

In another embodiment, the travel performance metric is an effective-travel ratio (ETR) as shown below:

${ETR}_{{worker},{period}} = \frac{({Travel\_ Time})_{pick}}{({Travel\_ Time})_{total}}$

The ETR is the ratio of a worker's travel time that resulted in a pick to the worker's total travel time.

In some embodiments, it may be necessary to compute more than one travel-performance metric to assess a worker's performance fairly and accurately. In these cases, it may be useful to combine the computed metrics. For example, a weighted sum or average of performance metrics may be used to generate a new performance metric (i.e., a fused-performance metric). In another example, a ratio of metrics may be used to generate a new performance metric.

In many cases, it is important to assess a worker-performance using a travel-performance metric that is independent of the distance that a worker travels. This helps avoids confusion since each worker may be assigned different work tasks and since each worker may take different routes to travel from location-to-location. Since a worker may perform many movements during a measurement period (e.g., a work shift), the time-variations resulting from a worker's different routes may be averaged to compute a fair travel-performance metric. In addition, a travel-performance metric may be computed by comparing a worker's travel times for movements only to other workers that performed the same movements in order to make a fair comparison. In addition, a travel performance metric may be computed by comparing a worker's travel times for movements to an equivalent time that a population would be expected to perform the same movements. In these ways, the worker's time to perform a long distance movement is not unfairly compared to times taken to perform short distance movements (i.e., distance independent). A distance-independent (i.e., travel-pattern based) travel-performance metric may be computed in a variety of ways.

In one embodiment, the travel-performance metric is computed by comparing (i) a worker's average time taken to perform a location-to-location movement with (ii) the average time that the population of workers took to perform the same location-to-location movement.

In this embodiment, the location-to-location worker-travel times for a measurement period are obtained from the voice dialog (e.g., as shown in FIG. 3). Next, for each location-to-location movement a population-average-travel time is computed (i.e., Tpop_(loci,locj)). The population-average-travel time is the average time that all workers in the population took to perform a location-to-location movement (during the measurement period). This average time includes the times from all workers and from each instance that a particular worker performed the location-to-location movement. Next, for a worker the worker-average-travel time is computed for each location-to-location movement (i.e., Tworker_(loci,locj)). The worker-average-travel time for a particular location-to-location movement is the average time that a worker took to perform the location-to-location movement (during the measurement period).

By way of example, FIG. 4 illustrates which data is used to compute the population-average-travel time and the worker-average-travel time for a particular worker (i.e., “Worker2”) and a particular location-to-location movement (i.e., “ab”).

A travel-performance metric (i.e., TPM) for a worker may be computed by averaging the differences between the worker-average-travel time and the population-average-travel time for all location-to-location travels as shown below. TPM_(worker)=Average(Tpop_(loci,locj) −Tworker_(loci,locj))_(all loci,locj)

In another embodiment, the travel-performance metric may be computed by comparing (i) a worker's total time spent performing location-to-location movements with (ii) the total time that the population of workers would be expected to perform the same location-to-location movements.

As before, the location-to-location worker-travel times for a measurement period are obtained from the voice dialog (e.g., as shown in FIG. 3). For each location-to-location movement a population-average-travel time is computed (i.e., Tpop_(loci,locj)). Then, for a worker the worker-total-travel time is computed for each location-to-location movement (i.e., TOTworker_(loci,locj)). The worker-total-travel time for a particular location-to-location movement is the total time (i.e., sum of the worker-travel times) that the worker took to perform the location-to-location movement. Next, the number of times that the worker performed the location-to-location movement is recorded (i.e., N_(loci,locj)). Then, using the population-average-travel time and the number of times each location-to-location movement was perform, a corresponding population-total-travel time (i.e., TOTpop_(loci,locj)) is computed for each location-to-location movement as shown below. TOTpop_(loci,locj) =N _(loci,locj) ×Tpop_(loci,locj)

By way of example, FIG. 5 illustrates which data is used to compute the population-total-travel time and the worker-total-travel time for a particular worker (i.e., “Worker2”) and a particular location-to-location movement (i.e., “ab”).

A travel-performance metric (i.e., TPM) for a worker may be computed as the difference between the worker-total-travel time and the population-total-travel time divided by the total number of movements performed by the worker for all location-to-location travels as shown below.

${TPM}_{worker} = \frac{\left( {{\sum_{{{all}\mspace{14mu}{loci}},{locj}}{TOT}_{{pop}_{{loci},{locj}}}} - {\sum_{{{all}\mspace{14mu}{loci}},{locj}}{TOT}_{{worker}_{{loci},{locj}}}}} \right)}{\sum_{{{all}\mspace{14mu}{loci}},{locj}}N_{{loci},{locj}}}$

The performance of a particular worker may be assessed by comparing the travel-performance metric for a particular worker to the travel-performance metrics of other workers. For example, workers in a population of workers may be ranked by their performance metric. In this case, a worker's performance may be assessed by their rank or other grouping (e.g., quartile). In some cases, a fused ranking may be created from the combination of the ranks of different performance metrics. For example, a fused ranking may be generated through a weighted sum of the rankings of different performance metrics.

Graphical reports may be created based on a worker's travel-performance metric and/or the assessment of the worker's performance (e.g., the worker's rank). FIG. 6 illustrates an exemplary report of worker's travel-performance metrics. Here, workers are identified on the X-axis by number while the vertical axis displays each worker's travel-performance metric. Various reports (e.g., tables, graphs, charts, etc.) and various views (e.g., bar graphs, pie charts, etc.) of each report are envisioned by the present disclosure, and not limited to the example shown in FIG. 6. The graphical reports may be generated for view on a computing device (e.g., computer, smartphone, tablet, etc.) with a display.

Alerts may be created based on a worker's travel-performance metric and/or the assessment of the worker's performance (e.g., the worker's rank). These alerts may include messages presented or sent to a particular worker (e.g., a supervisor) and are typically intended to generate a response. For example, an alert message may be sent (e.g., text message, email message) to a supervisor stating that a worker needs attention (e.g., additional training, discipline, encouragement, etc.) as a result of the worker's performance. In another example, a voice message may be communicated directly to the worker (via the worker's voice-enabled mobile terminal), based on the worker's performance.

The travel-performance metrics and/or the reports/alerts may be stored in a database on a computer-readable readable memory for future viewing and/or future use (e.g., for comparison with performance metrics created in the future). The data in the database may be filtered to generate various reports (e.g., by worker/group, by measurement period, by movement, by location, by item picked etc.).

Filtering by measurement period enables the assessment performance by weekday, weekend, weekly, monthly, and specific dates (e.g., before a major holiday). For example, workers who work on weekday may be compared against those who work on weekend. In another example, a worker's performance may be assessed weekly or monthly. In still another example, a worker's performance during a period of high demand may be compared to periods having normal work conditions.

Filtering by worker/group also enables the assessment of performance by aspects of the worker/group. For example, a group may include workers of a particular experience level or workers using a particular language.

Filtering by location also enables the assessment of worker performance based aspects of the location. For example, a particular location-to-location route may be compared with other routes.

Filtering by item may enable the assessment of worker performance based on aspects of an item picked. These aspects may include items that are bulk, palletized, or packaged in containers.

In some embodiments, actions may be taken based on the performance of a worker. For example, the work tasks that are assigned to a worker may be based on the assessed performance of the worker. If a worker's travel performance is low, for example, then the worker may only be assigned short location-to-location movements.

To supplement the present disclosure, this application incorporates entirely by reference the following commonly assigned patents, patent application publications, and patent applications:

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In the specification and/or figures, typical embodiments of the invention have been disclosed. The present invention is not limited to such exemplary embodiments. The use of the term “and/or” includes any and all combinations of one or more of the associated listed items. The figures are schematic representations and so are not necessarily drawn to scale. Unless otherwise noted, specific terms have been used in a generic and descriptive sense and not for purposes of limitation. 

The invention claimed is:
 1. A worker-management system, comprising: a host computer located in a first facility and in wireless communication with a voice-enabled mobile terminal comprising a headset worn by a worker, wherein the host computer manages the voice-enabled mobile terminal facilitating a voice dialog between the worker and the host computer to enable voice-directed movement throughout a second facility, wherein the host computer includes a processor configured by software to: (i) receive, from a wireless communication link, electronic audio communication data corresponding to a voice dialog recorded with timestamps during a measurement period from a microphone of the voice-enabled mobile terminal; (ii) process the electronic audio communication data to determine the voice dialog; (iii) parse, utilizing speech-recognition, at least a first portion of the voice dialog to identify a first keyword or a first phrase and identify at least a first constituent component indicating a first location where the worker is located based at least in part on identification of the first keyword or the first phrase; (iv) identify a second constituent component comprising a first timestamp associated with a travel prompt provided via the voice-enabled mobile terminal, wherein the travel prompt directs the worker to a second location; (v) parse, utilizing speech-recognition, at least a second portion of the voice dialog to isolate a relevant portion of the voice dialog by at least identifying, utilizing speech-recognition, a second keyword or a second phrase indicating the worker has moved to the second location, and identify from the relevant portion a third constituent component comprising a second timestamp based on identification of the second keyword or the second phrase; (vi) programmatically calculate the worker-travel time based at least in part on the first timestamp and the second timestamp; (vii) based at least in part on the worker-travel time from the first location to the second location and a model retrieved from the host computer's memory, programmatically compute travel-performance metrics for the worker, wherein the travel-performance metrics corresponds to a travel-to-pick ratio, a travel-work ratio, and an effective-travel ratio, (viii) assign ranks to each of the travel-performance metrics of the worker, wherein the ranks associated with the different travel-performance metrics are combined to create a fused rank, (ix) assess a performance of the worker using the fused rank of the worker, (x) based on the travel-performance metrics and the assessed performance using the fused rank of the worker, the processor is further configured to: generate, via a display, a graphical report comprising filters to sort data represented in the graphical report, and generate, via the voice-enabled mobile terminal, an alert comprising a voice prompt to the worker.
 2. The worker-management system according to claim 1, wherein computing the travel-performance metrics for each of the worker comprises: computing the worker-total-travel time for all of the worker's work tasks; and computing the travel-pick ratio that is the ratio of the worker-total-travel time to a total number of the worker's work tasks.
 3. The worker-management system according to claim 1, wherein computing the travel-performance metric for the worker comprises: computing the travel-work ratio that is the ratio of the worker-total-travel time to the worker time spent otherwise.
 4. The worker-management system according to claim 1, wherein computing the travel-performance metrics for the worker comprises: computing the effective-travel ratio that is the ratio of the worker-travel time that resulted in a pick to the worker-total-travel time.
 5. The worker-management system according to claim 1, wherein the processor is further configured by software to: create a voice message for the worker based on the performance assessment; and transmit the voice message from the host computer to the worker's voice-enabled mobile terminal.
 6. The worker-management system according to claim 1, the host computer is further configured to: store, in at least one database, the worker-travel time associated with data indicating movement from the first location to the second location, wherein the travel-performance metrics of the worker is determined based at least in part on at least one other worker-travel times corresponding to at least one other worker and retrieved from the at least one database based at least in part on the data indicating movement from the first location to the second location.
 7. The worker-management system according to claim 1, wherein to identify the second constituent component comprising the first timestamp associated with the travel prompt, the host computer is configured to: parse, utilizing speech recognition, at least a third portion of the voice dialog to identify a third keyword or a third phrase and identify the second constituent component based at least in part on identification of the third keyword or the third phrase.
 8. The worker-management system according to claim 1, wherein computing the travel-performance metrics for the worker comprises: receiving data from a wireless communication link representing voice dialogs from a plurality of workers associated with voice-enabled mobile terminals in the second facility during the measurement period; assessing the performance of the worker by comparing the travel-performance metric for the worker to another travel-performance metric for another worker; computing a worker-average-travel time that is an average of the worker-travel time obtained from the worker for the location-to-location movement; computing a population-average-travel time that is an average of the worker-travel time obtained from all workers in a population of workers for the location-to-location movement; and comparing the worker-average-travel time to the population-average-travel time.
 9. The worker-management system according to claim 8, wherein computing the travel-performance metrics for the worker comprises: computing the worker-total-travel time that is a sum of the worker-travel time obtained from the worker for the location-to-location movement; counting number of times the worker performed the location-to-location movement; computing the population-total-travel time that is the average of the worker-travel time obtained from all workers in the population of workers for the location-to-location movement multiplied by the number of times the worker performed the location-to-location movement; and comparing the worker-total-travel time to the population-total-travel time.
 10. The worker-management system according to claim 8, wherein the population of workers is a subset of all workers that performed work during the measurement period.
 11. The worker-management system according to claim 8, further comprising: the display communicatively coupled to the host computer for presenting reports and/or alerts based on the assessment.
 12. The worker-management system according to claim 11, wherein the reports and/or alerts include the ranking of workers by the travel-performance metrics.
 13. The worker-management system according to claim 11, wherein the reports and/or alerts include a message that the worker needs attention regarding worker's performance.
 14. A method for assessing a worker's performance in a voice-enabled workflow, the method comprising the steps of: receiving, from a wireless communication link, at a host computer in a first facility, electronic audio communication data corresponding to a voice dialog recorded with timestamps during a measurement period from a microphone of a voice-enabled mobile terminal in a second facility; processing the electronic audio communication data to determine the voice dialog; parsing, utilizing speech-recognition, at least a first portion of the voice dialog to identify a first keyword or a first phrase and identify at least a first constituent component indicating a first location where the worker is located based at least in part on identification of the first keyword or the first phrase; identifying a second constituent component comprising a first timestamp associated with a travel prompt provided via the voice-enabled mobile terminal, wherein the travel prompt directs the worker to a second location; parse, utilizing speech-recognition, at least a second portion of the voice dialog to isolate a relevant portion of the voice dialog by at least identifying, utilizing speech-recognition, a second keyword or a second phrase associated with a first location-to-location movement indicating the worker has moved to the second location, and identify from the relevant portion a third constituent component comprising a second timestamp based on identification of the second keyword or the second phrase; programmatically calculate the worker-travel time based at least in part on the first timestamp and the second timestamp; repeating the steps of receiving and analyzing to obtain the worker-travel time for each other in a population of workers; creating a population-average-travel time for each location-to-location movement, wherein the population-average-travel time for a particular location-to-location movement is the average of all worker-travel time obtained from the population of workers for the particular location-to-location movement; calculating, using the worker-travel time and the population-average-travel time, travel-performance metrics for each worker, wherein the travel-performance metrics corresponds to a travel-to-pick ratio, a travel-work ratio, and an effective-travel ratio; assigning ranks to each of the travel-performance metrics of each worker, wherein the ranks associated with the different travel-performance metrics are combined to create a fused rank; assessing a performance of the worker using the fused rank of the worker; and based on the travel-performance metrics and the assessed performance using the fused rank of the worker, generating, via a display, a graphical report comprising filters to sort data represented in the graphical report, and generating, via the voice-enabled mobile terminal, an alert comprising a voice prompt to the worker.
 15. The method according to claim 14, wherein the step of calculating the travel-performance metric for each worker comprises: computing a worker-average-travel time that is an average of the worker-travel time obtained from the worker for the particular location-to-location movement; computing the average difference between the worker-average-travel time and the population-average-travel-time for all location-to-location movements.
 16. The method according to claim 14, wherein the step of calculating the travel-performance metric for each worker comprises: creating the worker-total-travel time for each location-to-location movement, wherein the worker-total-travel time for a particular location-to-location movement is the sum of the worker's worker-travel time obtained for the particular location-to-location movement; counting, number of times each location-to-location movement was performed; creating a population-total-travel time for each location-to-location movement, wherein a worker's population-total-travel time for a particular location-to-location movement is the number of times the particular location-to-location movement was performed by the worker multiplied by the population-average-travel time for the particular location-to-location movement; and calculating the travel-performance metrics as the difference between the sum of the worker-total-travel time for all location-to-location movement and the sum of the population-total-travel time for all location-to-location movement divided by the total number of location-to-location movement performed by the worker during the measurement period.
 17. The method according to claim 14, further comprises: receiving, at the host computer in the first facility, plurality of voice dialogs corresponding to plurality of workers voice-enabled workflow; assessing the worker's performance by comparing the worker's travel-performance metric to the travel-performance metrics for other workers in the population of workers; and combining the worker's travel-performance metrics with other performance metrics to generate a new performance metric.
 18. The method according to claim 17, wherein the step of assessing the worker's performance comprises: comparing the travel-performance metric for a worker obtained during the measurement period to a travel-performance metrics for the worker obtained during a different measurement period.
 19. The method according to claim 17, further comprising the steps of: generating the graphical report comprising the results of the assessment; and transmitting the graphical report to a computing device, wherein the computing device comprises the display for displaying the graphical report.
 20. The method according to claim 17, further comprising the step of: adjusting the work tasks assigned to a worker based on the assessment of the worker's performance. 